Effective Well Rate Management Using a Streamline-Based Model in Belridge Diatomite Waterflood

Abstract

In the early 1980's, waterflooding began in Belridge Diatomite (BD), a thick, highly porous, and tight reservoir in California. Over the years, differing injection strategies lead to mixed results. Today, there are approximately 1,900+ injectors and 2,600+ producers in the Belridge Diatomite Waterflood (BDWF) and setting injection and production well rate targets to improve recovery is extremely challenging. Multiple teams using different methods for estimating rate targets in different parts of the field adds complexity to the overall field management strategy. The main objective of this work was to simplify field management by using a quantitative, streamline-based method to establish and quantify injector-producer relationships, reduce human bias, while improving the efficiency and flood performance in the West Grande (WG) area of the BDWF subject to key surface and subsurface constraints. The pilot test targeted 52 injection strings and 142 producers in Southern WG with an approximate total water injection rate of 10,500 STB/Day and total water and oil production rates of 8,200 STB/D and 700 STB/D, respectively. An important and novel aspect of the work presented here involves constraining the injection well rate targets to surface elevation, injection string communication, 30-day average wellhead pressure, subsurface impairments, and surface injection capacity. On the production side, the constraints were surface elevation, pump runtime, and operational status. This work describes setting well rate targets using a streamline-based workflow while honoring minimum and maximum rates as deduced from the constraints above. Injection and production rates in the WG pilot area were changed four times over nine months in the period May 2020-January 2021. Not withstanding uncertainties in production and watercut measurements and interference due to operational activities, the injection and production rate changes resulted in a reversal of the oil decline observed in the previous year. The improvement in the oil decline honored all key constraints and did not cause WG to experience changes to surface elevation, an important requirement in reservoir management of the highly compressible Diatomite reservoir. Using streamlines to define well patterns based on historical production/injection rates, and considering all patterns simultaneously is a major departure from the one-at-a-time fixed pattern and reservoir team-specific strategy used in the past. Considering that the manual pattern-by-pattern review consumed well over 50% of the time spent by teams trying to improve flood performance, the approach described in the work also represents a significant improvement in productivity and a more agile reservoir management strategy. The ability to include key surface and subsurface constraints in the calculation of well target rates is a novel addition to streamline-based surveillance modeling and a key contribution of this work.